library(tidyverse)
── Attaching core tidyverse packages ──────────────────────────────────────────────────────────────────────── tidyverse 2.0.0 ──
✔ dplyr     1.1.4     ✔ readr     2.1.5
✔ forcats   1.0.0     ✔ stringr   1.5.1
✔ ggplot2   3.4.4     ✔ tibble    3.2.1
✔ lubridate 1.9.3     ✔ tidyr     1.3.0
✔ purrr     1.0.2     ── Conflicts ────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
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✖ dplyr::lag()    masks stats::lag()
ℹ Use the ]8;;http://conflicted.r-lib.org/conflicted package]8;; to force all conflicts to become errors
library(ggplot2)
library(tsibble)

Attaching package: ‘tsibble’

The following object is masked from ‘package:lubridate’:

    interval

The following objects are masked from ‘package:base’:

    intersect, setdiff, union
library(fabletools)
library(readxl)
library(openxlsx)

Descripción de AMZN

Amazon es una de las empresas tecnológicas y de comercio electrónico más grandes del mundo, fundada en 1994 por Jeff Bezos en Seattle, Washington. Inicialmente comenzó como una tienda en línea de libros, pero ha crecido exponencialmente para ofrecer una amplia gama de productos y servicios, incluyendo electrónicos, ropa, alimentos, servicios en la nube, entretenimiento digital, inteligencia artificial y dispositivos de consumo. La empresa se ha expandido globalmente, con presencia en múltiples países y regiones. Amazon es conocida por su enfoque en la innovación, la eficiencia operativa y la atención al cliente. Su plataforma de comercio electrónico, Amazon.com, es una de las más visitadas del mundo. Además, Amazon Web Services (AWS) es uno de los proveedores líderes de servicios en la nube, brindando infraestructura informática a empresas y organizaciones de todos los tamaños. Amazon también ha incursionado en la producción de contenido original a través de su división de entretenimiento, Amazon Studios, y ha desarrollado una serie de dispositivos de hardware populares, como los altavoces inteligentes Echo y los lectores de libros electrónicos Kindle. La empresa está constantemente buscando expandirse en nuevos mercados y áreas de negocio, y su impacto en la economía y la sociedad global es significativo.

Datos

Importar

amzn_csv <- read.csv("AMZN.csv")

Cambiar tipo de datos

amzn_csv$Date = ymd(amzn_csv$Date)

Crear tsibble

amzn <- amzn_csv %>% 
  as_tsibble(index = Date)
amzn
class(amzn)
[1] "tbl_ts"     "tbl_df"     "tbl"        "data.frame"

Análisis

Visualización de la serie

Time plot

amzn |>
  autoplot(Close) + 
  labs(y = "Prices", title = "Historic Prices AMZN")

NA
NA

Patrones y Estacionalidad

feasts::autoplot(amzn) + ggtitle('Historical graphic AMZN') + ylab('Prices') + xlab('Date')
Plot variable not specified, automatically selected `.vars = Open`

library(tsibble)
library(feasts)
library(plotly)
Registered S3 method overwritten by 'data.table':
  method           from
  print.data.table     
Registered S3 method overwritten by 'htmlwidgets':
  method           from         
  print.htmlwidget tools:rstudio

Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout
amzn$Adj.Close = as.numeric(amzn$Adj.Close)
head(amzn, 2)
amzn_filled <- fill_gaps(amzn)
amzn_filled %>%
  gg_season(Adj.Close, labels = "both") +
  ggtitle('Historical graphic AMZN') + 
  ylab('Price') + xlab('Date')

yearly_amzn_plot = amzn_filled %>% gg_season(Adj.Close, labels = "both") +
    ggtitle('Yearly historical graphic AMZN ') + ylab('Date') + xlab('Price')

ggplotly(yearly_amzn_plot)

Gráfico de Rezagos

lags_plots = amzn_filled  %>% 
  filter(year(Date) > 2022) %>% 
  gg_lag(Adj.Close, geom = "point", lags = 1:12) + 
  labs(x ="lag(Precio, k)")
Warning: Removed 113 rows containing missing values (gg_lag).
suppressWarnings(ggplotly(lags_plots))
amzn_filled %>% ACF(Adj.Close, lag_max = 12)

Autocorrelación

amzn_filled %>% ACF(Adj.Close, lag_max = 24) %>% autoplot() + labs(title='Historical price AMZN')

Estadística descriptiva

summary(amzn$Adj.Close, 'value')
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
  0.2985   2.2695  10.2720  37.9091  59.6800 186.5705 

Medidas de dispersión

library(EnvStats)

Attaching package: ‘EnvStats’

The following objects are masked from ‘package:stats’:

    predict, predict.lm
kurtosis(amzn$Adj.Close)
[1] 0.5085591
skewness(amzn$Adj.Close)
[1] 1.364781
sd(amzn$Adj.Close)
[1] 51.5428
var(amzn$Adj.Close)
[1] 2656.66
library(ggExtra)
p <- ggplot(amzn_filled, aes(x=Date, y=Adj.Close)) + 
        geom_hline(yintercept =25) + 
        geom_hline(yintercept =150) +
        geom_point() + 
        ggtitle('Historical graphic AMZN') + ylab('Price') + xlab('Date')

ggMarginal(p, type='histogram', margins = 'y')
Warning: Removed 2725 rows containing missing values (`geom_point()`).Warning: Removed 2725 rows containing missing values (`geom_point()`).

histogram = ggplot(amzn_filled, aes(x = Adj.Close)) +
  geom_histogram( bins = 20, fill = "black", color = "black", alpha = 0.5) +
  labs(title = "Histograma",
       x = "Price",
       y = "Densidad")

ggplotly(histogram)
Warning: Removed 2725 rows containing non-finite values (`stat_bin()`).

Valores atípicos

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